Overview

Dataset statistics

Number of variables33
Number of observations179941
Missing cells1642712
Missing cells (%)27.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.1 MiB
Average record size in memory257.0 B

Variable types

Text8
Numeric18
Boolean1
Categorical5
DateTime1

Alerts

explicit is highly imbalanced (53.2%)Imbalance
time_signature is highly imbalanced (69.4%)Imbalance
streams has 177889 (98.9%) missing valuesMissing
track_artists has 160063 (89.0%) missing valuesMissing
chart has 177503 (98.6%) missing valuesMissing
added_at has 96031 (53.4%) missing valuesMissing
track_album_album has 160052 (88.9%) missing valuesMissing
duration_ms has 177503 (98.6%) missing valuesMissing
track_track_number has 160052 (88.9%) missing valuesMissing
rank has 177503 (98.6%) missing valuesMissing
region has 177503 (98.6%) missing valuesMissing
trend has 177503 (98.6%) missing valuesMissing
track_id has unique valuesUnique
artist_popularity has 3658 (2.0%) zerosZeros
key has 21568 (12.0%) zerosZeros
popularity has 41619 (23.1%) zerosZeros
instrumentalness has 41636 (23.1%) zerosZeros

Reproduction

Analysis started2024-06-20 14:45:15.374145
Analysis finished2024-06-20 14:46:00.525210
Duration45.15 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

track_id
Text

UNIQUE 

Distinct179941
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:00.719731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3958702
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179941 ?
Unique (%)100.0%

Sample

1st row07vS8obfeZbr8H4MgQfXR7
2nd row1PEqh7awkpuepLBSq8ZwqD
3rd row7E8pPgBY84oDaXRcqODavR
4th row0Atml4huw4Fgyk6YSHiK4M
5th row4WYDmIZrwxBHdBYdvi5oQO
ValueCountFrequency (%)
07vs8obfezbr8h4mgqfxr7 1
 
< 0.1%
05hypqpzzxyvirt2gsbvut 1
 
< 0.1%
0fzg0n1pelolvlofc0gvez 1
 
< 0.1%
3ogkp5i1ohdoudticpfina 1
 
< 0.1%
7e8ppgby84odaxrcqodavr 1
 
< 0.1%
0atml4huw4fgyk6yshik4m 1
 
< 0.1%
4wydmizrwxbhdbydvi5oqo 1
 
< 0.1%
0awg4a7t5urmzz4pzvnav3 1
 
< 0.1%
1dihssbztad6qj5ttee90f 1
 
< 0.1%
2uwnp6tzvvmtovzx5elooy 1
 
< 0.1%
Other values (179931) 179931
> 99.9%
2024-06-20T17:46:01.204091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 84551
 
2.1%
4 84517
 
2.1%
6 84196
 
2.1%
1 84146
 
2.1%
2 84088
 
2.1%
3 83840
 
2.1%
5 83341
 
2.1%
7 79640
 
2.0%
l 61607
 
1.6%
w 61499
 
1.6%
Other values (52) 3167277
80.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3958702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 84551
 
2.1%
4 84517
 
2.1%
6 84196
 
2.1%
1 84146
 
2.1%
2 84088
 
2.1%
3 83840
 
2.1%
5 83341
 
2.1%
7 79640
 
2.0%
l 61607
 
1.6%
w 61499
 
1.6%
Other values (52) 3167277
80.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3958702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 84551
 
2.1%
4 84517
 
2.1%
6 84196
 
2.1%
1 84146
 
2.1%
2 84088
 
2.1%
3 83840
 
2.1%
5 83341
 
2.1%
7 79640
 
2.0%
l 61607
 
1.6%
w 61499
 
1.6%
Other values (52) 3167277
80.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3958702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 84551
 
2.1%
4 84517
 
2.1%
6 84196
 
2.1%
1 84146
 
2.1%
2 84088
 
2.1%
3 83840
 
2.1%
5 83341
 
2.1%
7 79640
 
2.0%
l 61607
 
1.6%
w 61499
 
1.6%
Other values (52) 3167277
80.0%

streams
Real number (ℝ)

MISSING 

Distinct1981
Distinct (%)96.5%
Missing177889
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean34400.06
Minimum1004
Maximum637771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:01.335528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1004
5-th percentile1568.75
Q14346.25
median16783
Q339132.75
95-th percentile143104.35
Maximum637771
Range636767
Interquartile range (IQR)34786.5

Descriptive statistics

Standard deviation53686.331
Coefficient of variation (CV)1.5606464
Kurtosis20.289467
Mean34400.06
Median Absolute Deviation (MAD)13702.5
Skewness3.7315636
Sum70588924
Variance2.8822222 × 109
MonotonicityNot monotonic
2024-06-20T17:46:01.464352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3999 4
 
< 0.1%
3597 3
 
< 0.1%
2690 3
 
< 0.1%
20010 2
 
< 0.1%
5075 2
 
< 0.1%
1185 2
 
< 0.1%
1188 2
 
< 0.1%
3212 2
 
< 0.1%
48445 2
 
< 0.1%
1043 2
 
< 0.1%
Other values (1971) 2028
 
1.1%
(Missing) 177889
98.9%
ValueCountFrequency (%)
1004 1
< 0.1%
1006 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1016 1
< 0.1%
1020 1
< 0.1%
1021 1
< 0.1%
1024 1
< 0.1%
1026 1
< 0.1%
1029 2
< 0.1%
ValueCountFrequency (%)
637771 1
< 0.1%
478668 1
< 0.1%
477835 1
< 0.1%
406721 1
< 0.1%
392564 1
< 0.1%
360712 1
< 0.1%
340756 1
< 0.1%
339036 1
< 0.1%
336768 1
< 0.1%
310816 1
< 0.1%

artist_followers
Real number (ℝ)

Distinct35817
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2778310.1
Minimum0
Maximum1.1375993 × 108
Zeros135
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:01.589849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile224
Q17005
median97424
Q31089769
95-th percentile11922459
Maximum1.1375993 × 108
Range1.1375993 × 108
Interquartile range (IQR)1082764

Descriptive statistics

Standard deviation10220758
Coefficient of variation (CV)3.678768
Kurtosis58.357139
Mean2778310.1
Median Absolute Deviation (MAD)97018
Skewness7.0508955
Sum4.999319 × 1011
Variance1.044639 × 1014
MonotonicityNot monotonic
2024-06-20T17:46:01.721290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4284731 685
 
0.4%
5328848 511
 
0.3%
108717184 409
 
0.2%
5201016 344
 
0.2%
2448813 267
 
0.1%
3236824 248
 
0.1%
25456 245
 
0.1%
1320080 244
 
0.1%
86988887 231
 
0.1%
49487244 220
 
0.1%
Other values (35807) 176537
98.1%
ValueCountFrequency (%)
0 135
0.1%
1 69
< 0.1%
2 67
< 0.1%
3 148
0.1%
4 63
< 0.1%
5 52
 
< 0.1%
6 66
< 0.1%
7 61
< 0.1%
8 66
< 0.1%
9 51
 
< 0.1%
ValueCountFrequency (%)
113759927 106
 
0.1%
108717184 409
0.2%
107854251 16
 
< 0.1%
95921722 118
 
0.1%
93515140 60
 
< 0.1%
86988887 231
0.1%
83371408 142
 
0.1%
82467148 132
 
0.1%
80598088 72
 
< 0.1%
74608422 117
 
0.1%

genres
Text

Distinct18242
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:01.938951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length254
Median length188
Mean length36.002884
Min length2

Characters and Unicode

Total characters6478395
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6249 ?
Unique (%)3.5%

Sample

1st row['indie pop', 'la indie', 'pov: indie']
2nd row['lilith', 'new wave pop']
3rd row['deep groove house', 'house', 'tech house']
4th row[]
5th row['chill lounge', 'deep chill']
ValueCountFrequency (%)
rock 50803
 
6.4%
pop 46272
 
5.8%
32453
 
4.1%
hip 17033
 
2.2%
hop 16912
 
2.1%
rap 16258
 
2.1%
metal 15078
 
1.9%
indie 13412
 
1.7%
jazz 12974
 
1.6%
house 12464
 
1.6%
Other values (2660) 557986
70.5%
2024-06-20T17:46:02.304809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 826924
 
12.8%
611704
 
9.4%
a 418615
 
6.5%
o 407926
 
6.3%
e 369969
 
5.7%
r 349668
 
5.4%
i 296188
 
4.6%
n 294731
 
4.5%
, 266296
 
4.1%
c 265882
 
4.1%
Other values (34) 2370492
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6478395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 826924
 
12.8%
611704
 
9.4%
a 418615
 
6.5%
o 407926
 
6.3%
e 369969
 
5.7%
r 349668
 
5.4%
i 296188
 
4.6%
n 294731
 
4.5%
, 266296
 
4.1%
c 265882
 
4.1%
Other values (34) 2370492
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6478395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 826924
 
12.8%
611704
 
9.4%
a 418615
 
6.5%
o 407926
 
6.3%
e 369969
 
5.7%
r 349668
 
5.4%
i 296188
 
4.6%
n 294731
 
4.5%
, 266296
 
4.1%
c 265882
 
4.1%
Other values (34) 2370492
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6478395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 826924
 
12.8%
611704
 
9.4%
a 418615
 
6.5%
o 407926
 
6.3%
e 369969
 
5.7%
r 349668
 
5.4%
i 296188
 
4.6%
n 294731
 
4.5%
, 266296
 
4.1%
c 265882
 
4.1%
Other values (34) 2370492
36.6%

album_total_tracks
Real number (ℝ)

Distinct232
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.429958
Minimum1
Maximum930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:02.435794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median11
Q316
95-th percentile38
Maximum930
Range929
Interquartile range (IQR)14

Descriptive statistics

Standard deviation21.893174
Coefficient of variation (CV)1.6301745
Kurtosis557.59192
Mean13.429958
Median Absolute Deviation (MAD)7
Skewness16.670209
Sum2416600
Variance479.31107
MonotonicityNot monotonic
2024-06-20T17:46:02.559631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 40687
22.6%
12 13564
 
7.5%
10 12238
 
6.8%
11 9862
 
5.5%
14 8341
 
4.6%
13 8302
 
4.6%
15 7460
 
4.1%
2 6064
 
3.4%
16 5993
 
3.3%
9 4531
 
2.5%
Other values (222) 62899
35.0%
ValueCountFrequency (%)
1 40687
22.6%
2 6064
 
3.4%
3 3383
 
1.9%
4 4289
 
2.4%
5 3799
 
2.1%
6 3520
 
2.0%
7 3375
 
1.9%
8 4095
 
2.3%
9 4531
 
2.5%
10 12238
 
6.8%
ValueCountFrequency (%)
930 29
< 0.1%
579 1
 
< 0.1%
578 1
 
< 0.1%
545 2
 
< 0.1%
531 1
 
< 0.1%
484 5
 
< 0.1%
463 3
 
< 0.1%
460 1
 
< 0.1%
455 1
 
< 0.1%
453 1
 
< 0.1%

track_artists
Text

MISSING 

Distinct13294
Distinct (%)66.9%
Missing160063
Missing (%)89.0%
Memory size1.4 MiB
2024-06-20T17:46:02.824169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length79
Median length58
Mean length11.843344
Min length1

Characters and Unicode

Total characters235422
Distinct characters520
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10512 ?
Unique (%)52.9%

Sample

1st rowPhoebe Bridgers
2nd rowOliver Cheatham
3rd rowWillie Nelson
4th rowFlorence + The Machine
5th rowLudwig Ahgren
ValueCountFrequency (%)
the 1150
 
2.9%
398
 
1.0%
of 157
 
0.4%
de 138
 
0.4%
john 135
 
0.3%
music 127
 
0.3%
band 109
 
0.3%
and 101
 
0.3%
van 100
 
0.3%
david 99
 
0.3%
Other values (14750) 36607
93.6%
2024-06-20T17:46:03.312433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 20594
 
8.7%
19244
 
8.2%
a 18888
 
8.0%
i 13943
 
5.9%
n 13701
 
5.8%
o 13461
 
5.7%
r 12955
 
5.5%
l 10298
 
4.4%
s 9481
 
4.0%
t 8480
 
3.6%
Other values (510) 94377
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 235422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 20594
 
8.7%
19244
 
8.2%
a 18888
 
8.0%
i 13943
 
5.9%
n 13701
 
5.8%
o 13461
 
5.7%
r 12955
 
5.5%
l 10298
 
4.4%
s 9481
 
4.0%
t 8480
 
3.6%
Other values (510) 94377
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 235422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 20594
 
8.7%
19244
 
8.2%
a 18888
 
8.0%
i 13943
 
5.9%
n 13701
 
5.8%
o 13461
 
5.7%
r 12955
 
5.5%
l 10298
 
4.4%
s 9481
 
4.0%
t 8480
 
3.6%
Other values (510) 94377
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 235422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 20594
 
8.7%
19244
 
8.2%
a 18888
 
8.0%
i 13943
 
5.9%
n 13701
 
5.8%
o 13461
 
5.7%
r 12955
 
5.5%
l 10298
 
4.4%
s 9481
 
4.0%
t 8480
 
3.6%
Other values (510) 94377
40.1%

artist_popularity
Real number (ℝ)

ZEROS 

Distinct93
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.129253
Minimum0
Maximum100
Zeros3658
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:03.473424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q131
median47
Q361
95-th percentile78
Maximum100
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.37483
Coefficient of variation (CV)0.47363579
Kurtosis-0.56884533
Mean45.129253
Median Absolute Deviation (MAD)15
Skewness-0.22345695
Sum8120603
Variance456.88335
MonotonicityNot monotonic
2024-06-20T17:46:03.616155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 3848
 
2.1%
0 3658
 
2.0%
58 3558
 
2.0%
52 3555
 
2.0%
45 3418
 
1.9%
53 3170
 
1.8%
38 3140
 
1.7%
44 3105
 
1.7%
54 3075
 
1.7%
55 3072
 
1.7%
Other values (83) 146342
81.3%
ValueCountFrequency (%)
0 3658
2.0%
1 1467
0.8%
2 1245
 
0.7%
3 1026
 
0.6%
4 966
 
0.5%
5 975
 
0.5%
6 1025
 
0.6%
7 964
 
0.5%
8 872
 
0.5%
9 906
 
0.5%
ValueCountFrequency (%)
100 409
0.2%
93 231
 
0.1%
91 294
 
0.2%
90 379
0.2%
88 136
 
0.1%
87 611
0.3%
86 343
0.2%
85 499
0.3%
84 719
0.4%
83 785
0.4%

explicit
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size175.9 KiB
False
162012 
True
17929 
ValueCountFrequency (%)
False 162012
90.0%
True 17929
 
10.0%
2024-06-20T17:46:03.735663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

tempo
Real number (ℝ)

Distinct74885
Distinct (%)41.6%
Missing78
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean119.19542
Minimum0
Maximum244.44
Zeros92
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:03.846488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.2082
Q195.489
median119.982
Q3138.3405
95-th percentile174.025
Maximum244.44
Range244.44
Interquartile range (IQR)42.8515

Descriptive statistics

Standard deviation30.433036
Coefficient of variation (CV)0.25532051
Kurtosis-0.2583071
Mean119.19542
Median Absolute Deviation (MAD)21.023
Skewness0.29313823
Sum21438847
Variance926.16969
MonotonicityNot monotonic
2024-06-20T17:46:03.980323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92
 
0.1%
120.003 66
 
< 0.1%
119.999 65
 
< 0.1%
120.006 57
 
< 0.1%
128.001 56
 
< 0.1%
120.012 55
 
< 0.1%
120.007 55
 
< 0.1%
119.991 54
 
< 0.1%
127.999 53
 
< 0.1%
120.002 53
 
< 0.1%
Other values (74875) 179257
99.6%
(Missing) 78
 
< 0.1%
ValueCountFrequency (%)
0 92
0.1%
30.121 1
 
< 0.1%
30.862 1
 
< 0.1%
31.419 1
 
< 0.1%
32.54 1
 
< 0.1%
32.979 1
 
< 0.1%
33.615 1
 
< 0.1%
33.62 1
 
< 0.1%
33.714 1
 
< 0.1%
33.829 1
 
< 0.1%
ValueCountFrequency (%)
244.44 1
< 0.1%
244.08 1
< 0.1%
243.372 1
< 0.1%
240 1
< 0.1%
236.196 1
< 0.1%
236.059 1
< 0.1%
233.933 1
< 0.1%
232.489 1
< 0.1%
231.831 1
< 0.1%
230.03 1
< 0.1%

chart
Categorical

MISSING 

Distinct2
Distinct (%)0.1%
Missing177503
Missing (%)98.6%
Memory size1.4 MiB
top200
2052 
viral50
386 

Length

Max length7
Median length6
Mean length6.1583265
Min length6

Characters and Unicode

Total characters15014
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtop200
2nd rowtop200
3rd rowtop200
4th rowtop200
5th rowtop200

Common Values

ValueCountFrequency (%)
top200 2052
 
1.1%
viral50 386
 
0.2%
(Missing) 177503
98.6%

Length

2024-06-20T17:46:04.097445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T17:46:04.182206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
top200 2052
84.2%
viral50 386
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 4490
29.9%
t 2052
13.7%
o 2052
13.7%
p 2052
13.7%
2 2052
13.7%
v 386
 
2.6%
i 386
 
2.6%
r 386
 
2.6%
a 386
 
2.6%
l 386
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4490
29.9%
t 2052
13.7%
o 2052
13.7%
p 2052
13.7%
2 2052
13.7%
v 386
 
2.6%
i 386
 
2.6%
r 386
 
2.6%
a 386
 
2.6%
l 386
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4490
29.9%
t 2052
13.7%
o 2052
13.7%
p 2052
13.7%
2 2052
13.7%
v 386
 
2.6%
i 386
 
2.6%
r 386
 
2.6%
a 386
 
2.6%
l 386
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4490
29.9%
t 2052
13.7%
o 2052
13.7%
p 2052
13.7%
2 2052
13.7%
v 386
 
2.6%
i 386
 
2.6%
r 386
 
2.6%
a 386
 
2.6%
l 386
 
2.6%
Distinct12032
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:04.397769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.625744
Min length4

Characters and Unicode

Total characters1732066
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2757 ?
Unique (%)1.5%

Sample

1st row2018-12-05
2nd row1996-04-16
3rd row2014-07-07
4th row2001-01-24
5th row2014-10-03
ValueCountFrequency (%)
2013-01-01 927
 
0.5%
2007-01-01 815
 
0.5%
2006-01-01 757
 
0.4%
2010-01-01 731
 
0.4%
2011-01-01 730
 
0.4%
2012-01-01 722
 
0.4%
2008-01-01 722
 
0.4%
2009-01-01 699
 
0.4%
2005-01-01 666
 
0.4%
2014-01-01 597
 
0.3%
Other values (12022) 172575
95.9%
2024-06-20T17:46:04.758738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 421674
24.3%
- 337434
19.5%
2 329009
19.0%
1 282461
16.3%
9 81914
 
4.7%
3 62949
 
3.6%
8 47209
 
2.7%
7 45137
 
2.6%
6 42457
 
2.5%
4 41362
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1732066
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 421674
24.3%
- 337434
19.5%
2 329009
19.0%
1 282461
16.3%
9 81914
 
4.7%
3 62949
 
3.6%
8 47209
 
2.7%
7 45137
 
2.6%
6 42457
 
2.5%
4 41362
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1732066
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 421674
24.3%
- 337434
19.5%
2 329009
19.0%
1 282461
16.3%
9 81914
 
4.7%
3 62949
 
3.6%
8 47209
 
2.7%
7 45137
 
2.6%
6 42457
 
2.5%
4 41362
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1732066
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 421674
24.3%
- 337434
19.5%
2 329009
19.0%
1 282461
16.3%
9 81914
 
4.7%
3 62949
 
3.6%
8 47209
 
2.7%
7 45137
 
2.6%
6 42457
 
2.5%
4 41362
 
2.4%

energy
Real number (ℝ)

Distinct2742
Distinct (%)1.5%
Missing78
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.53822196
Minimum0
Maximum1
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:04.897328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0421
Q10.302
median0.574
Q30.784
95-th percentile0.947
Maximum1
Range1
Interquartile range (IQR)0.482

Descriptive statistics

Standard deviation0.28719625
Coefficient of variation (CV)0.53360187
Kurtosis-1.0988891
Mean0.53822196
Median Absolute Deviation (MAD)0.234
Skewness-0.26854908
Sum96806.216
Variance0.082481684
MonotonicityNot monotonic
2024-06-20T17:46:05.164932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.725 268
 
0.1%
0.675 266
 
0.1%
0.829 264
 
0.1%
0.67 263
 
0.1%
0.873 260
 
0.1%
0.719 257
 
0.1%
0.676 256
 
0.1%
0.695 256
 
0.1%
0.789 255
 
0.1%
0.831 255
 
0.1%
Other values (2732) 177263
98.5%
ValueCountFrequency (%)
0 4
< 0.1%
1.7 × 10-51
 
< 0.1%
2.02 × 10-52
< 0.1%
2.03 × 10-54
< 0.1%
4.19 × 10-51
 
< 0.1%
8.36 × 10-51
 
< 0.1%
9.62 × 10-51
 
< 0.1%
0.000116 1
 
< 0.1%
0.000127 1
 
< 0.1%
0.000143 1
 
< 0.1%
ValueCountFrequency (%)
1 63
 
< 0.1%
0.999 90
0.1%
0.998 113
0.1%
0.997 108
0.1%
0.996 118
0.1%
0.995 149
0.1%
0.994 135
0.1%
0.993 159
0.1%
0.992 126
0.1%
0.991 158
0.1%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing78
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.2187776
Minimum0
Maximum11
Zeros21568
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:05.270186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5594175
Coefficient of variation (CV)0.68204045
Kurtosis-1.2903028
Mean5.2187776
Median Absolute Deviation (MAD)3
Skewness0.018584767
Sum938665
Variance12.669453
MonotonicityNot monotonic
2024-06-20T17:46:05.364545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 21568
12.0%
7 20559
11.4%
2 19044
10.6%
9 17662
9.8%
1 17055
9.5%
5 15745
8.8%
4 13264
7.4%
11 13256
7.4%
10 12244
6.8%
8 11683
6.5%
Other values (2) 17783
9.9%
ValueCountFrequency (%)
0 21568
12.0%
1 17055
9.5%
2 19044
10.6%
3 6516
 
3.6%
4 13264
7.4%
5 15745
8.8%
6 11267
6.3%
7 20559
11.4%
8 11683
6.5%
9 17662
9.8%
ValueCountFrequency (%)
11 13256
7.4%
10 12244
6.8%
9 17662
9.8%
8 11683
6.5%
7 20559
11.4%
6 11267
6.3%
5 15745
8.8%
4 13264
7.4%
3 6516
 
3.6%
2 19044
10.6%

added_at
Date

MISSING 

Distinct64381
Distinct (%)76.7%
Missing96031
Missing (%)53.4%
Memory size1.4 MiB
Minimum1970-01-01 00:00:00+00:00
Maximum2024-03-09 17:50:01+00:00
2024-06-20T17:46:05.482327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:46:05.632076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

popularity
Real number (ℝ)

ZEROS 

Distinct99
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.723609
Minimum0
Maximum98
Zeros41619
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:05.768562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median24
Q343
95-th percentile65
Maximum98
Range98
Interquartile range (IQR)42

Descriptive statistics

Standard deviation22.437244
Coefficient of variation (CV)0.87224324
Kurtosis-0.9089024
Mean25.723609
Median Absolute Deviation (MAD)21
Skewness0.42918483
Sum4628732
Variance503.42993
MonotonicityNot monotonic
2024-06-20T17:46:05.894154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41619
 
23.1%
1 3709
 
2.1%
2 2717
 
1.5%
31 2520
 
1.4%
38 2515
 
1.4%
35 2490
 
1.4%
40 2480
 
1.4%
29 2470
 
1.4%
30 2469
 
1.4%
32 2469
 
1.4%
Other values (89) 114483
63.6%
ValueCountFrequency (%)
0 41619
23.1%
1 3709
 
2.1%
2 2717
 
1.5%
3 2363
 
1.3%
4 2212
 
1.2%
5 1931
 
1.1%
6 1789
 
1.0%
7 1662
 
0.9%
8 1686
 
0.9%
9 1814
 
1.0%
ValueCountFrequency (%)
98 2
 
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
95 2
 
< 0.1%
94 3
 
< 0.1%
93 6
 
< 0.1%
92 2
 
< 0.1%
91 14
< 0.1%
90 8
 
< 0.1%
89 20
< 0.1%

track_album_album
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing160052
Missing (%)88.9%
Memory size1.4 MiB
album
11386 
single
6710 
compilation
1793 

Length

Max length11
Median length5
Mean length5.8782744
Min length5

Characters and Unicode

Total characters116913
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsingle
2nd rowcompilation
3rd rowalbum
4th rowalbum
5th rowsingle

Common Values

ValueCountFrequency (%)
album 11386
 
6.3%
single 6710
 
3.7%
compilation 1793
 
1.0%
(Missing) 160052
88.9%

Length

2024-06-20T17:46:06.033673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T17:46:06.152333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
album 11386
57.2%
single 6710
33.7%
compilation 1793
 
9.0%

Most occurring characters

ValueCountFrequency (%)
l 19889
17.0%
a 13179
11.3%
m 13179
11.3%
b 11386
9.7%
u 11386
9.7%
i 10296
8.8%
n 8503
7.3%
s 6710
 
5.7%
g 6710
 
5.7%
e 6710
 
5.7%
Other values (4) 8965
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116913
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 19889
17.0%
a 13179
11.3%
m 13179
11.3%
b 11386
9.7%
u 11386
9.7%
i 10296
8.8%
n 8503
7.3%
s 6710
 
5.7%
g 6710
 
5.7%
e 6710
 
5.7%
Other values (4) 8965
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116913
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 19889
17.0%
a 13179
11.3%
m 13179
11.3%
b 11386
9.7%
u 11386
9.7%
i 10296
8.8%
n 8503
7.3%
s 6710
 
5.7%
g 6710
 
5.7%
e 6710
 
5.7%
Other values (4) 8965
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116913
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 19889
17.0%
a 13179
11.3%
m 13179
11.3%
b 11386
9.7%
u 11386
9.7%
i 10296
8.8%
n 8503
7.3%
s 6710
 
5.7%
g 6710
 
5.7%
e 6710
 
5.7%
Other values (4) 8965
7.7%

duration_ms
Real number (ℝ)

MISSING 

Distinct2307
Distinct (%)94.6%
Missing177503
Missing (%)98.6%
Infinite0
Infinite (%)0.0%
Mean222597.67
Minimum0
Maximum1037586
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:06.292536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile151494.35
Q1190687.25
median215312.5
Q3246770
95-th percentile309981.95
Maximum1037586
Range1037586
Interquartile range (IQR)56082.75

Descriptive statistics

Standard deviation57831.365
Coefficient of variation (CV)0.2598022
Kurtosis28.234144
Mean222597.67
Median Absolute Deviation (MAD)27487
Skewness2.7975409
Sum5.4269312 × 108
Variance3.3444668 × 109
MonotonicityNot monotonic
2024-06-20T17:46:06.443918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
< 0.1%
193893 5
 
< 0.1%
194080 3
 
< 0.1%
180000 3
 
< 0.1%
240000 3
 
< 0.1%
207186 3
 
< 0.1%
216773 3
 
< 0.1%
177000 2
 
< 0.1%
232786 2
 
< 0.1%
264066 2
 
< 0.1%
Other values (2297) 2406
 
1.3%
(Missing) 177503
98.6%
ValueCountFrequency (%)
0 6
< 0.1%
46768 1
 
< 0.1%
60453 1
 
< 0.1%
78680 1
 
< 0.1%
80927 1
 
< 0.1%
81160 1
 
< 0.1%
89769 1
 
< 0.1%
90539 1
 
< 0.1%
95466 1
 
< 0.1%
96400 1
 
< 0.1%
ValueCountFrequency (%)
1037586 1
< 0.1%
913214 1
< 0.1%
589094 1
< 0.1%
579293 1
< 0.1%
547106 1
< 0.1%
544626 1
< 0.1%
537506 1
< 0.1%
530253 1
< 0.1%
510476 1
< 0.1%
507306 1
< 0.1%
Distinct5317
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:06.737212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1110
Median length1104
Mean length812.72987
Min length2

Characters and Unicode

Total characters146243426
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3197 ?
Unique (%)1.8%

Sample

1st row[]
2nd row['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']
3rd row[]
4th row[]
5th row['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']
ValueCountFrequency (%)
cw 134184
 
0.5%
pt 133566
 
0.5%
no 133526
 
0.5%
fi 133522
 
0.5%
es 133516
 
0.5%
hu 133460
 
0.5%
nl 133425
 
0.5%
cr 133417
 
0.5%
za 133396
 
0.5%
lt 133371
 
0.5%
Other values (176) 23065178
94.5%
2024-06-20T17:46:07.197137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 48721152
33.3%
, 24220620
16.6%
24220620
16.6%
M 3684329
 
2.5%
G 3034096
 
2.1%
T 3030971
 
2.1%
S 2896130
 
2.0%
A 2774959
 
1.9%
B 2756613
 
1.9%
L 2508624
 
1.7%
Other values (21) 28395312
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 146243426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 48721152
33.3%
, 24220620
16.6%
24220620
16.6%
M 3684329
 
2.5%
G 3034096
 
2.1%
T 3030971
 
2.1%
S 2896130
 
2.0%
A 2774959
 
1.9%
B 2756613
 
1.9%
L 2508624
 
1.7%
Other values (21) 28395312
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 146243426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 48721152
33.3%
, 24220620
16.6%
24220620
16.6%
M 3684329
 
2.5%
G 3034096
 
2.1%
T 3030971
 
2.1%
S 2896130
 
2.0%
A 2774959
 
1.9%
B 2756613
 
1.9%
L 2508624
 
1.7%
Other values (21) 28395312
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 146243426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 48721152
33.3%
, 24220620
16.6%
24220620
16.6%
M 3684329
 
2.5%
G 3034096
 
2.1%
T 3030971
 
2.1%
S 2896130
 
2.0%
A 2774959
 
1.9%
B 2756613
 
1.9%
L 2508624
 
1.7%
Other values (21) 28395312
19.4%

track_track_number
Real number (ℝ)

MISSING 

Distinct90
Distinct (%)0.5%
Missing160052
Missing (%)88.9%
Infinite0
Infinite (%)0.0%
Mean5.4239529
Minimum1
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:07.329196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile16
Maximum188
Range187
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.1224466
Coefficient of variation (CV)1.3131468
Kurtosis60.643267
Mean5.4239529
Median Absolute Deviation (MAD)2
Skewness5.334544
Sum107877
Variance50.729245
MonotonicityNot monotonic
2024-06-20T17:46:07.471849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7242
 
4.0%
2 1921
 
1.1%
3 1591
 
0.9%
4 1264
 
0.7%
5 1022
 
0.6%
6 1005
 
0.6%
7 871
 
0.5%
8 728
 
0.4%
9 719
 
0.4%
10 611
 
0.3%
Other values (80) 2915
 
1.6%
(Missing) 160052
88.9%
ValueCountFrequency (%)
1 7242
4.0%
2 1921
 
1.1%
3 1591
 
0.9%
4 1264
 
0.7%
5 1022
 
0.6%
6 1005
 
0.6%
7 871
 
0.5%
8 728
 
0.4%
9 719
 
0.4%
10 611
 
0.3%
ValueCountFrequency (%)
188 1
< 0.1%
134 1
< 0.1%
120 1
< 0.1%
100 1
< 0.1%
98 1
< 0.1%
97 1
< 0.1%
96 1
< 0.1%
92 1
< 0.1%
91 1
< 0.1%
89 2
< 0.1%

rank
Real number (ℝ)

MISSING 

Distinct200
Distinct (%)8.2%
Missing177503
Missing (%)98.6%
Infinite0
Infinite (%)0.0%
Mean114.49754
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:07.624680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q150
median124
Q3170
95-th percentile196
Maximum200
Range199
Interquartile range (IQR)120

Descriptive statistics

Standard deviation61.018121
Coefficient of variation (CV)0.53292081
Kurtosis-1.3398603
Mean114.49754
Median Absolute Deviation (MAD)55
Skewness-0.23066025
Sum279145
Variance3723.2111
MonotonicityNot monotonic
2024-06-20T17:46:07.766123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198 34
 
< 0.1%
49 34
 
< 0.1%
47 33
 
< 0.1%
50 32
 
< 0.1%
197 32
 
< 0.1%
188 29
 
< 0.1%
200 28
 
< 0.1%
196 28
 
< 0.1%
195 27
 
< 0.1%
45 26
 
< 0.1%
Other values (190) 2135
 
1.2%
(Missing) 177503
98.6%
ValueCountFrequency (%)
1 5
 
< 0.1%
2 7
< 0.1%
3 6
 
< 0.1%
4 6
 
< 0.1%
5 8
< 0.1%
6 6
 
< 0.1%
7 5
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
10 15
< 0.1%
ValueCountFrequency (%)
200 28
< 0.1%
199 23
< 0.1%
198 34
< 0.1%
197 32
< 0.1%
196 28
< 0.1%
195 27
< 0.1%
194 22
< 0.1%
193 21
< 0.1%
192 18
< 0.1%
191 17
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing78
Missing (%)< 0.1%
Memory size1.4 MiB
1.0
112686 
0.0
67177 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters539589
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 112686
62.6%
0.0 67177
37.3%
(Missing) 78
 
< 0.1%

Length

2024-06-20T17:46:07.913424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T17:46:08.021514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 112686
62.7%
0.0 67177
37.3%

Most occurring characters

ValueCountFrequency (%)
0 247040
45.8%
. 179863
33.3%
1 112686
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 539589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 247040
45.8%
. 179863
33.3%
1 112686
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 539589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 247040
45.8%
. 179863
33.3%
1 112686
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 539589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 247040
45.8%
. 179863
33.3%
1 112686
20.9%

time_signature
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing78
Missing (%)< 0.1%
Memory size1.4 MiB
4.0
155605 
3.0
18558 
5.0
 
3618
1.0
 
1983
0.0
 
99

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters539589
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 155605
86.5%
3.0 18558
 
10.3%
5.0 3618
 
2.0%
1.0 1983
 
1.1%
0.0 99
 
0.1%
(Missing) 78
 
< 0.1%

Length

2024-06-20T17:46:08.138885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T17:46:08.247240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4.0 155605
86.5%
3.0 18558
 
10.3%
5.0 3618
 
2.0%
1.0 1983
 
1.1%
0.0 99
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 179962
33.4%
. 179863
33.3%
4 155605
28.8%
3 18558
 
3.4%
5 3618
 
0.7%
1 1983
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 539589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 179962
33.4%
. 179863
33.3%
4 155605
28.8%
3 18558
 
3.4%
5 3618
 
0.7%
1 1983
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 539589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 179962
33.4%
. 179863
33.3%
4 155605
28.8%
3 18558
 
3.4%
5 3618
 
0.7%
1 1983
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 539589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 179962
33.4%
. 179863
33.3%
4 155605
28.8%
3 18558
 
3.4%
5 3618
 
0.7%
1 1983
 
0.4%
Distinct113541
Distinct (%)63.1%
Missing81
Missing (%)< 0.1%
Memory size1.4 MiB
2024-06-20T17:46:08.533781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length230
Median length162
Mean length20.951957
Min length1

Characters and Unicode

Total characters3768419
Distinct characters1954
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86336 ?
Unique (%)48.0%

Sample

1st rowSpotify Singles
2nd rowNow in a Minute
3rd rowLove Too Deep
4th rowVoces Del Pueblo
5th rowThe Smooth Operator - Cosmopolitan Lounge Music
ValueCountFrequency (%)
the 28813
 
4.5%
16240
 
2.5%
of 11573
 
1.8%
a 5816
 
0.9%
vol 5334
 
0.8%
in 4941
 
0.8%
original 4651
 
0.7%
music 4318
 
0.7%
and 4287
 
0.7%
soundtrack 4133
 
0.6%
Other values (57592) 552509
86.0%
2024-06-20T17:46:08.982669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
462744
 
12.3%
e 323124
 
8.6%
o 221096
 
5.9%
a 218107
 
5.8%
i 210336
 
5.6%
n 189529
 
5.0%
r 173405
 
4.6%
t 166537
 
4.4%
s 156150
 
4.1%
l 133177
 
3.5%
Other values (1944) 1514214
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3768419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
462744
 
12.3%
e 323124
 
8.6%
o 221096
 
5.9%
a 218107
 
5.8%
i 210336
 
5.6%
n 189529
 
5.0%
r 173405
 
4.6%
t 166537
 
4.4%
s 156150
 
4.1%
l 133177
 
3.5%
Other values (1944) 1514214
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3768419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
462744
 
12.3%
e 323124
 
8.6%
o 221096
 
5.9%
a 218107
 
5.8%
i 210336
 
5.6%
n 189529
 
5.0%
r 173405
 
4.6%
t 166537
 
4.4%
s 156150
 
4.1%
l 133177
 
3.5%
Other values (1944) 1514214
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3768419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
462744
 
12.3%
e 323124
 
8.6%
o 221096
 
5.9%
a 218107
 
5.8%
i 210336
 
5.6%
n 189529
 
5.0%
r 173405
 
4.6%
t 166537
 
4.4%
s 156150
 
4.1%
l 133177
 
3.5%
Other values (1944) 1514214
40.2%

speechiness
Real number (ℝ)

Distinct1554
Distinct (%)0.9%
Missing78
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.080612429
Minimum0
Maximum0.964
Zeros92
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:09.129103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0286
Q10.0361
median0.0466
Q30.0771
95-th percentile0.278
Maximum0.964
Range0.964
Interquartile range (IQR)0.041

Descriptive statistics

Standard deviation0.092042637
Coefficient of variation (CV)1.1417921
Kurtosis20.200887
Mean0.080612429
Median Absolute Deviation (MAD)0.0136
Skewness3.7736607
Sum14499.193
Variance0.0084718471
MonotonicityNot monotonic
2024-06-20T17:46:09.421205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0365 581
 
0.3%
0.0343 579
 
0.3%
0.032 577
 
0.3%
0.0367 559
 
0.3%
0.0357 556
 
0.3%
0.0352 556
 
0.3%
0.0337 545
 
0.3%
0.0336 541
 
0.3%
0.0361 538
 
0.3%
0.0351 537
 
0.3%
Other values (1544) 174294
96.9%
ValueCountFrequency (%)
0 92
0.1%
0.022 1
 
< 0.1%
0.0223 4
 
< 0.1%
0.0224 4
 
< 0.1%
0.0225 5
 
< 0.1%
0.0226 2
 
< 0.1%
0.0227 5
 
< 0.1%
0.0228 11
 
< 0.1%
0.0229 8
 
< 0.1%
0.023 5
 
< 0.1%
ValueCountFrequency (%)
0.964 2
 
< 0.1%
0.963 1
 
< 0.1%
0.961 2
 
< 0.1%
0.96 4
< 0.1%
0.959 2
 
< 0.1%
0.958 6
< 0.1%
0.957 4
< 0.1%
0.956 7
< 0.1%
0.955 1
 
< 0.1%
0.954 5
< 0.1%

region
Text

MISSING 

Distinct64
Distinct (%)2.6%
Missing177503
Missing (%)98.6%
Memory size1.4 MiB
2024-06-20T17:46:09.602123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length18
Median length13
Mean length8.2042658
Min length4

Characters and Unicode

Total characters20002
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowSaudi Arabia
3rd rowAustralia
4th rowAustralia
5th rowHonduras
ValueCountFrequency (%)
australia 292
 
10.5%
argentina 190
 
6.8%
ireland 144
 
5.2%
united 141
 
5.1%
canada 125
 
4.5%
austria 96
 
3.5%
belgium 79
 
2.8%
republic 72
 
2.6%
states 72
 
2.6%
brazil 69
 
2.5%
Other values (62) 1495
53.9%
2024-06-20T17:46:09.936833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2834
14.2%
i 1825
 
9.1%
n 1708
 
8.5%
e 1483
 
7.4%
r 1294
 
6.5%
l 1140
 
5.7%
t 1062
 
5.3%
d 843
 
4.2%
u 828
 
4.1%
s 707
 
3.5%
Other values (36) 6278
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2834
14.2%
i 1825
 
9.1%
n 1708
 
8.5%
e 1483
 
7.4%
r 1294
 
6.5%
l 1140
 
5.7%
t 1062
 
5.3%
d 843
 
4.2%
u 828
 
4.1%
s 707
 
3.5%
Other values (36) 6278
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2834
14.2%
i 1825
 
9.1%
n 1708
 
8.5%
e 1483
 
7.4%
r 1294
 
6.5%
l 1140
 
5.7%
t 1062
 
5.3%
d 843
 
4.2%
u 828
 
4.1%
s 707
 
3.5%
Other values (36) 6278
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2834
14.2%
i 1825
 
9.1%
n 1708
 
8.5%
e 1483
 
7.4%
r 1294
 
6.5%
l 1140
 
5.7%
t 1062
 
5.3%
d 843
 
4.2%
u 828
 
4.1%
s 707
 
3.5%
Other values (36) 6278
31.4%

danceability
Real number (ℝ)

Distinct1312
Distinct (%)0.7%
Missing78
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.55507529
Minimum0
Maximum0.988
Zeros92
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:10.082804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.193
Q10.431
median0.576
Q30.698
95-th percentile0.834
Maximum0.988
Range0.988
Interquartile range (IQR)0.267

Descriptive statistics

Standard deviation0.19078175
Coefficient of variation (CV)0.34370428
Kurtosis-0.41361544
Mean0.55507529
Median Absolute Deviation (MAD)0.132
Skewness-0.41291019
Sum99837.506
Variance0.036397677
MonotonicityNot monotonic
2024-06-20T17:46:10.226962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.677 424
 
0.2%
0.641 421
 
0.2%
0.676 403
 
0.2%
0.632 399
 
0.2%
0.612 397
 
0.2%
0.686 395
 
0.2%
0.622 395
 
0.2%
0.679 394
 
0.2%
0.664 394
 
0.2%
0.64 393
 
0.2%
Other values (1302) 175848
97.7%
ValueCountFrequency (%)
0 92
0.1%
0.0532 1
 
< 0.1%
0.0535 1
 
< 0.1%
0.0543 2
 
< 0.1%
0.0553 1
 
< 0.1%
0.0554 2
 
< 0.1%
0.0555 1
 
< 0.1%
0.0556 1
 
< 0.1%
0.0559 2
 
< 0.1%
0.0562 1
 
< 0.1%
ValueCountFrequency (%)
0.988 1
 
< 0.1%
0.987 4
< 0.1%
0.986 2
 
< 0.1%
0.985 6
< 0.1%
0.984 3
 
< 0.1%
0.983 3
 
< 0.1%
0.981 6
< 0.1%
0.98 5
< 0.1%
0.979 4
< 0.1%
0.978 9
< 0.1%

valence
Real number (ℝ)

Distinct1782
Distinct (%)1.0%
Missing78
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.43840187
Minimum0
Maximum1
Zeros192
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:10.363962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0426
Q10.204
median0.413
Q30.655
95-th percentile0.906
Maximum1
Range1
Interquartile range (IQR)0.451

Descriptive statistics

Standard deviation0.26992129
Coefficient of variation (CV)0.61569374
Kurtosis-1.054741
Mean0.43840187
Median Absolute Deviation (MAD)0.223
Skewness0.24460305
Sum78852.275
Variance0.0728575
MonotonicityNot monotonic
2024-06-20T17:46:10.506852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 456
 
0.3%
0.962 370
 
0.2%
0.964 339
 
0.2%
0.963 336
 
0.2%
0.156 268
 
0.1%
0.198 265
 
0.1%
0.193 265
 
0.1%
0.196 264
 
0.1%
0.181 264
 
0.1%
0.158 263
 
0.1%
Other values (1772) 176773
98.2%
ValueCountFrequency (%)
0 192
0.1%
1 × 10-5144
0.1%
0.000253 1
 
< 0.1%
0.000479 1
 
< 0.1%
0.000532 1
 
< 0.1%
0.000605 1
 
< 0.1%
0.000823 1
 
< 0.1%
0.00129 1
 
< 0.1%
0.00165 1
 
< 0.1%
0.0018 1
 
< 0.1%
ValueCountFrequency (%)
1 7
< 0.1%
0.999 3
 
< 0.1%
0.996 4
 
< 0.1%
0.995 2
 
< 0.1%
0.994 1
 
< 0.1%
0.992 4
 
< 0.1%
0.991 4
 
< 0.1%
0.99 11
< 0.1%
0.989 6
< 0.1%
0.988 9
< 0.1%

acousticness
Real number (ℝ)

Distinct4973
Distinct (%)2.8%
Missing78
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.40669595
Minimum0
Maximum0.996
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:10.640852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0005521
Q10.0375
median0.289
Q30.803
95-th percentile0.988
Maximum0.996
Range0.996
Interquartile range (IQR)0.7655

Descriptive statistics

Standard deviation0.37411952
Coefficient of variation (CV)0.91989979
Kurtosis-1.4872892
Mean0.40669595
Median Absolute Deviation (MAD)0.28228
Skewness0.36914669
Sum73149.553
Variance0.13996541
MonotonicityNot monotonic
2024-06-20T17:46:10.778710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 1655
 
0.9%
0.994 1435
 
0.8%
0.993 1201
 
0.7%
0.992 1027
 
0.6%
0.991 939
 
0.5%
0.99 833
 
0.5%
0.989 772
 
0.4%
0.996 739
 
0.4%
0.988 701
 
0.4%
0.987 675
 
0.4%
Other values (4963) 169886
94.4%
ValueCountFrequency (%)
0 18
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-61
 
< 0.1%
1.04 × 10-62
 
< 0.1%
1.05 × 10-61
 
< 0.1%
1.08 × 10-61
 
< 0.1%
1.09 × 10-61
 
< 0.1%
1.11 × 10-61
 
< 0.1%
1.13 × 10-61
 
< 0.1%
1.14 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 739
0.4%
0.995 1655
0.9%
0.994 1435
0.8%
0.993 1201
0.7%
0.992 1027
0.6%
0.991 939
0.5%
0.99 833
0.5%
0.989 772
0.4%
0.988 701
0.4%
0.987 675
0.4%

liveness
Real number (ℝ)

Distinct1742
Distinct (%)1.0%
Missing78
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.18399709
Minimum0
Maximum1
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:10.911572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0625
Q10.096
median0.118
Q30.214
95-th percentile0.519
Maximum1
Range1
Interquartile range (IQR)0.118

Descriptive statistics

Standard deviation0.15883811
Coefficient of variation (CV)0.86326427
Kurtosis7.1281775
Mean0.18399709
Median Absolute Deviation (MAD)0.035
Skewness2.5085056
Sum33094.268
Variance0.025229545
MonotonicityNot monotonic
2024-06-20T17:46:11.047291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 2918
 
1.6%
0.11 2767
 
1.5%
0.108 2518
 
1.4%
0.109 2492
 
1.4%
0.107 2306
 
1.3%
0.112 2290
 
1.3%
0.106 2226
 
1.2%
0.105 2147
 
1.2%
0.102 2047
 
1.1%
0.104 2026
 
1.1%
Other values (1732) 156126
86.8%
ValueCountFrequency (%)
0 18
< 0.1%
0.00784 1
 
< 0.1%
0.0114 2
 
< 0.1%
0.0116 1
 
< 0.1%
0.012 3
 
< 0.1%
0.0126 1
 
< 0.1%
0.0133 1
 
< 0.1%
0.0136 1
 
< 0.1%
0.0137 1
 
< 0.1%
0.0142 1
 
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.999 1
 
< 0.1%
0.998 3
< 0.1%
0.997 2
 
< 0.1%
0.996 1
 
< 0.1%
0.995 3
< 0.1%
0.994 6
< 0.1%
0.993 7
< 0.1%
0.992 6
< 0.1%
0.991 6
< 0.1%

trend
Categorical

MISSING 

Distinct4
Distinct (%)0.2%
Missing177503
Missing (%)98.6%
Memory size1.4 MiB
NEW_ENTRY
1110 
MOVE_DOWN
706 
MOVE_UP
512 
SAME_POSITION
 
110

Length

Max length13
Median length9
Mean length8.7604594
Min length7

Characters and Unicode

Total characters21358
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEW_ENTRY
2nd rowMOVE_DOWN
3rd rowNEW_ENTRY
4th rowNEW_ENTRY
5th rowNEW_ENTRY

Common Values

ValueCountFrequency (%)
NEW_ENTRY 1110
 
0.6%
MOVE_DOWN 706
 
0.4%
MOVE_UP 512
 
0.3%
SAME_POSITION 110
 
0.1%
(Missing) 177503
98.6%

Length

2024-06-20T17:46:11.176340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T17:46:11.270971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
new_entry 1110
45.5%
move_down 706
29.0%
move_up 512
21.0%
same_position 110
 
4.5%

Most occurring characters

ValueCountFrequency (%)
E 3548
16.6%
N 3036
14.2%
_ 2438
11.4%
O 2144
10.0%
W 1816
8.5%
M 1328
 
6.2%
T 1220
 
5.7%
V 1218
 
5.7%
R 1110
 
5.2%
Y 1110
 
5.2%
Other values (6) 2390
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21358
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 3548
16.6%
N 3036
14.2%
_ 2438
11.4%
O 2144
10.0%
W 1816
8.5%
M 1328
 
6.2%
T 1220
 
5.7%
V 1218
 
5.7%
R 1110
 
5.2%
Y 1110
 
5.2%
Other values (6) 2390
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21358
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 3548
16.6%
N 3036
14.2%
_ 2438
11.4%
O 2144
10.0%
W 1816
8.5%
M 1328
 
6.2%
T 1220
 
5.7%
V 1218
 
5.7%
R 1110
 
5.2%
Y 1110
 
5.2%
Other values (6) 2390
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21358
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 3548
16.6%
N 3036
14.2%
_ 2438
11.4%
O 2144
10.0%
W 1816
8.5%
M 1328
 
6.2%
T 1220
 
5.7%
V 1218
 
5.7%
R 1110
 
5.2%
Y 1110
 
5.2%
Other values (6) 2390
11.2%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct5400
Distinct (%)3.0%
Missing78
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.29091744
Minimum0
Maximum1
Zeros41636
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-06-20T17:46:11.390840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.82 × 10-6
median0.00351
Q30.771
95-th percentile0.94
Maximum1
Range1
Interquartile range (IQR)0.77099818

Descriptive statistics

Standard deviation0.38917501
Coefficient of variation (CV)1.3377507
Kurtosis-1.2587199
Mean0.29091744
Median Absolute Deviation (MAD)0.00351
Skewness0.76681083
Sum52325.284
Variance0.15145719
MonotonicityNot monotonic
2024-06-20T17:46:11.524642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41636
 
23.1%
0.929 390
 
0.2%
0.916 386
 
0.2%
0.922 379
 
0.2%
0.918 368
 
0.2%
0.915 367
 
0.2%
0.934 367
 
0.2%
0.939 364
 
0.2%
0.931 363
 
0.2%
0.93 363
 
0.2%
Other values (5390) 134880
75.0%
ValueCountFrequency (%)
0 41636
23.1%
1 × 10-626
 
< 0.1%
1.01 × 10-659
 
< 0.1%
1.02 × 10-655
 
< 0.1%
1.03 × 10-653
 
< 0.1%
1.04 × 10-660
 
< 0.1%
1.05 × 10-644
 
< 0.1%
1.06 × 10-645
 
< 0.1%
1.07 × 10-654
 
< 0.1%
1.08 × 10-641
 
< 0.1%
ValueCountFrequency (%)
1 11
 
< 0.1%
0.999 7
 
< 0.1%
0.998 6
 
< 0.1%
0.997 10
 
< 0.1%
0.996 10
 
< 0.1%
0.995 18
< 0.1%
0.994 14
< 0.1%
0.993 19
< 0.1%
0.992 22
< 0.1%
0.991 31
< 0.1%

loudness
Real number (ℝ)

Distinct30093
Distinct (%)16.7%
Missing78
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-10.713653
Minimum-60
Maximum5.096
Zeros0
Zeros (%)0.0%
Negative179627
Negative (%)99.8%
Memory size1.4 MiB
2024-06-20T17:46:11.650191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-25.7059
Q1-13.43
median-8.558
Q3-5.885
95-th percentile-3.429
Maximum5.096
Range65.096
Interquartile range (IQR)7.545

Descriptive statistics

Standard deviation6.9642378
Coefficient of variation (CV)-0.65003394
Kurtosis2.2433973
Mean-10.713653
Median Absolute Deviation (MAD)3.275
Skewness-1.4825284
Sum-1926989.7
Variance48.500608
MonotonicityNot monotonic
2024-06-20T17:46:11.779104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.257 34
 
< 0.1%
-6.631 34
 
< 0.1%
-6.035 34
 
< 0.1%
-6.323 33
 
< 0.1%
-5.746 32
 
< 0.1%
-5.131 31
 
< 0.1%
-6.298 31
 
< 0.1%
-6.553 31
 
< 0.1%
-6.183 31
 
< 0.1%
-7.428 31
 
< 0.1%
Other values (30083) 179541
99.8%
(Missing) 78
 
< 0.1%
ValueCountFrequency (%)
-60 3
< 0.1%
-54.077 1
 
< 0.1%
-53.714 1
 
< 0.1%
-53.025 1
 
< 0.1%
-51.86 1
 
< 0.1%
-49.762 1
 
< 0.1%
-49.702 1
 
< 0.1%
-49.321 1
 
< 0.1%
-49.242 1
 
< 0.1%
-49.011 1
 
< 0.1%
ValueCountFrequency (%)
5.096 1
< 0.1%
3.97 1
< 0.1%
3.744 1
< 0.1%
3.554 1
< 0.1%
3.233 1
< 0.1%
3.221 1
< 0.1%
3.108 1
< 0.1%
2.985 1
< 0.1%
2.944 1
< 0.1%
2.882 1
< 0.1%

name
Text

Distinct140005
Distinct (%)77.8%
Missing93
Missing (%)0.1%
Memory size1.4 MiB
2024-06-20T17:46:12.069567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length254
Median length189
Mean length19.522986
Min length1

Characters and Unicode

Total characters3511170
Distinct characters2170
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123138 ?
Unique (%)68.5%

Sample

1st rowFriday I’m In Love - Recorded at Spotify Studios NYC
2nd rowI Love You Always Forever
3rd rowLove Too Deep - Radio Edit
4th rowNo Tiren Las Botellas
5th rowEl Momento de Despertar - Blue Sky Mix
ValueCountFrequency (%)
28693
 
4.4%
the 19977
 
3.1%
in 8868
 
1.4%
you 7764
 
1.2%
of 7331
 
1.1%
a 7047
 
1.1%
i 6840
 
1.0%
feat 5966
 
0.9%
me 5862
 
0.9%
no 4782
 
0.7%
Other values (68392) 550992
84.2%
2024-06-20T17:46:12.499362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
474268
 
13.5%
e 307528
 
8.8%
a 210858
 
6.0%
o 209818
 
6.0%
i 181500
 
5.2%
n 176149
 
5.0%
r 160236
 
4.6%
t 152010
 
4.3%
s 115173
 
3.3%
l 113186
 
3.2%
Other values (2160) 1410444
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3511170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
474268
 
13.5%
e 307528
 
8.8%
a 210858
 
6.0%
o 209818
 
6.0%
i 181500
 
5.2%
n 176149
 
5.0%
r 160236
 
4.6%
t 152010
 
4.3%
s 115173
 
3.3%
l 113186
 
3.2%
Other values (2160) 1410444
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3511170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
474268
 
13.5%
e 307528
 
8.8%
a 210858
 
6.0%
o 209818
 
6.0%
i 181500
 
5.2%
n 176149
 
5.0%
r 160236
 
4.6%
t 152010
 
4.3%
s 115173
 
3.3%
l 113186
 
3.2%
Other values (2160) 1410444
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3511170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
474268
 
13.5%
e 307528
 
8.8%
a 210858
 
6.0%
o 209818
 
6.0%
i 181500
 
5.2%
n 176149
 
5.0%
r 160236
 
4.6%
t 152010
 
4.3%
s 115173
 
3.3%
l 113186
 
3.2%
Other values (2160) 1410444
40.2%

Interactions

2024-06-20T17:45:56.093643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:26.796241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:28.387558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:30.096856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:31.799911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:33.669003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:35.377882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:37.087282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:38.931731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:40.620193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:42.174567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:43.906379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:45.516045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:47.379393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:49.089188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:50.791362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:52.660672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:54.369020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:56.202209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:26.889268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:28.487555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:30.196070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:31.900081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:33.771247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:35.482202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:37.187493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:39.026417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:40.708527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:42.267019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:43.997024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:45.618013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:47.484975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:49.187005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:50.894533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:52.760687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:54.469983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:56.318957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:26.979157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:28.584114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:30.290070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:31.996226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:33.868786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:35.580202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:37.282118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:39.122828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:40.794827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:42.498995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:44.089582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:45.714493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:47.584903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:49.287261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:50.991669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:52.856980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:54.569093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:56.555945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:27.071553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:28.676756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:30.379702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:32.087523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:33.966491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:35.676836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:37.377345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:39.215030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-20T17:45:46.997642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:48.713627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:50.417515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:52.283027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:53.990275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:55.710918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:57.735474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:28.126306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:29.813968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:31.520761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:33.387401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:35.097671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:36.808360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:38.649908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:40.341349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:41.920224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:43.637513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:45.248986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:47.097706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:48.810636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:50.511221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:52.377745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:54.088185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:55.807876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:57.835673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:28.212659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:29.913203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:31.615533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:33.483924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:35.193671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:36.902863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:38.748447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:40.436941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:42.003327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:43.725121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:45.335695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:47.194828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:48.906021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:50.608631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:52.476730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:54.183120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:55.904907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:57.937397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:28.298733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:30.008802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:31.713527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:33.581130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:35.294672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:37.002295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:38.843575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:40.535189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:42.089524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:43.815230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:45.424017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:47.291387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:49.001610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:50.705713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:52.573060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:54.282344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T17:45:56.003047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-20T17:45:58.146322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-20T17:45:58.750224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-20T17:45:59.822511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

track_idstreamsartist_followersgenresalbum_total_trackstrack_artistsartist_popularityexplicittempochartalbum_release_dateenergykeyadded_atpopularitytrack_album_albumduration_msavailable_marketstrack_track_numberrankmodetime_signaturealbum_namespeechinessregiondanceabilityvalenceacousticnesslivenesstrendinstrumentalnessloudnessname
007vS8obfeZbr8H4MgQfXR7NaN2338837.0['indie pop', 'la indie', 'pov: indie']2.0Phoebe Bridgers74.0False97.129NaN2018-12-050.1237.02021-07-04T11:06:43Z0.0singleNaN[]2.0NaN1.04.0Spotify Singles0.0407NaN0.3730.1380.94800.0816NaN0.000000-15.193Friday I’m In Love - Recorded at Spotify Studios NYC
11PEqh7awkpuepLBSq8ZwqD27156.084914.0['lilith', 'new wave pop']11.0NaN51.0False103.773top2001996-04-160.4535.02023-01-25T01:09:09Z71.0NaN239960.0['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaN187.01.04.0Now in a Minute0.0348France0.7440.1220.62700.0898NEW_ENTRY0.421000-11.977I Love You Always Forever
27E8pPgBY84oDaXRcqODavRNaN59150.0['deep groove house', 'house', 'tech house']2.0NaN54.0False122.030NaN2014-07-070.8789.0NaN0.0NaNNaN[]NaNNaN0.04.0Love Too Deep0.0357NaN0.7470.8970.07940.3700NaN0.000531-5.209Love Too Deep - Radio Edit
30Atml4huw4Fgyk6YSHiK4MNaN1528.0[]15.0NaN0.0False84.099NaN2001-01-240.4847.0NaN0.0NaNNaN[]NaNNaN1.04.0Voces Del Pueblo0.0356NaN0.6040.5640.10000.0865NaN0.000000-7.097No Tiren Las Botellas
44WYDmIZrwxBHdBYdvi5oQONaN6776.0['chill lounge', 'deep chill']26.0NaN28.0False156.017NaN2014-10-030.4470.0NaN7.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0The Smooth Operator - Cosmopolitan Lounge Music0.0613NaN0.7610.7610.06160.0822NaN0.873000-10.961El Momento de Despertar - Blue Sky Mix
50awG4a7t5UrmZZ4PZVNav3NaN28431.0['post-disco']29.0Oliver Cheatham50.0False116.545NaN2020-05-080.61111.02021-12-19T05:12:52Z0.0compilationNaN[]22.0NaN0.04.0Disco Essentials0.0573NaN0.8650.9630.22600.0572NaN0.082000-11.571Get Down Saturday Night
61DihsSBztAD6qJ5TTEE90FNaN355425.0['classic italian pop', 'italian adult pop']11.0NaN44.0False120.012NaN2015-02-120.63311.0NaN0.0NaNNaN[]NaNNaN0.04.0Naif0.0323NaN0.7130.5180.07850.1060NaN0.000003-7.425Senza fare sul serio
72uwnP6tZVVmTovzX5ELooyNaN23324247.0['conscious hip hop', 'hip hop', 'north carolina hip hop', 'rap']21.0NaN84.0True99.992NaN2013-06-180.6081.02023-10-03T23:26:09Z80.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0Born Sinner (Deluxe Version)0.2160NaN0.6670.4750.32400.4260NaN0.000198-7.054Power Trip (feat. Miguel)
805HYPQPzZXyvirt2GsbvutNaN2166585.0['classic country pop', 'classic texas country', 'country', 'country rock', 'nashville sound', 'outlaw country', 'singer-songwriter']11.0Willie Nelson69.0False80.972NaN2016-02-260.2172.0NaN18.0albumNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']9.0NaN1.04.0Summertime: Willie Nelson Sings Gershwin0.0326NaN0.5530.1970.84200.1010NaN0.000066-12.973Embraceable You (feat. Sheryl Crow)
93FUPP0Q5E2JKopEUHXIwdwNaN195.0[]3.0NaN31.0False89.206NaN2023-01-080.1562.0NaN35.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN0.04.0Somewhere Only We Go0.0320NaN0.4420.1820.98700.1860NaN0.900000-17.749Somewhere Only We Go
track_idstreamsartist_followersgenresalbum_total_trackstrack_artistsartist_popularityexplicittempochartalbum_release_dateenergykeyadded_atpopularitytrack_album_albumduration_msavailable_marketstrack_track_numberrankmodetime_signaturealbum_namespeechinessregiondanceabilityvalenceacousticnesslivenesstrendinstrumentalnessloudnessname
1799316rqVtroIGItaD2obcYl5KlNaN1835755.0['speedrun', 'video game music']1.0NaN74.0False154.925NaN2018-09-240.52302.02023-10-04T08:58:20Z25.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN0.04.02017 World Championship Theme0.0528NaN0.1550.3650.26500.0801NaN0.946000-12.7732017 World Championship Theme
1799324wNkd86QODa5bu5C04sCbyNaN10919.0['g-house']1.0NaN32.0False123.983NaN2021-11-050.676010.02022-07-01T01:30:24Z16.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN0.03.0Like That0.0850NaN0.7830.6930.05890.2970NaN0.000048-6.982Like That
1799330tQb1teNCNQAG5eH5iYus9NaN335216.0['soul jazz']9.0Quincy Jones52.0False120.172NaN1981-01-010.48508.02018-08-24T22:29:22Z0.0albumNaN[]6.0NaN1.04.0The Dude0.0496NaN0.8350.8270.17300.0410NaN0.000202-13.551Razzamatazz
17993458JksdJcOD3FbRqEq0fQpFNaN200.0[]30.0NaN6.0False79.893NaN2012-06-180.160010.02014-01-30T15:58:05Z2.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0Café Latino: Background Music, Lounge Café Sound Therapy, Latin Cocktail Bar Music Background, Waterfront Soft Party, Up Lifting Latin Music0.0335NaN0.6910.6510.97000.1100NaN0.502000-17.701Lounge
1799356zr4Nlzw7hSbcrg6cZjvJFNaN1918734.0['salsa', 'salsa puertorriquena', 'tropical']27.0NaN62.0False101.300NaN2012-03-260.60900.0NaN0.0NaNNaN[]NaNNaN1.04.0Anthology0.0313NaN0.6640.8220.59900.3660NaN0.000005-6.483Piraña
1799367uIwFnxujSlYuKuea2ti6aNaN622296.0['classical', 'classical era']63.0NaN52.0False55.838NaN2020-02-280.10309.02022-04-26T16:13:56Z19.0NaNNaN['AE', 'AR', 'AT', 'AU', 'BE', 'BG', 'BH', 'BO', 'BR', 'CA', 'CH', 'CI', 'CL', 'CO', 'CR', 'CW', 'CY', 'CZ', 'DE', 'DK', 'DO', 'DZ', 'EC', 'EE', 'EG', 'ES', 'FI', 'FR', 'GB', 'GH', 'GR', 'GT', 'HK', 'HN', 'HR', 'HU', 'ID', 'IE', 'IL', 'IN', 'IS', 'IT', 'JP', 'KE', 'KH', 'KR', 'KW', 'LB', 'LK', 'LT', 'LU', 'LV', 'MA', 'MT', 'MX', 'MY', 'NG', 'NI', 'NL', 'NO', 'NZ', 'OM', 'PA', 'PE', 'PH', 'PL', 'PT', 'PY', 'QA', 'RO', 'RS', 'SA', 'SE', 'SG', 'SI', 'SK', 'SV', 'TH', 'TR', 'TT', 'TW', 'UA', 'US', 'UY', 'VE', 'VN', 'XK', 'ZA']NaNNaN1.04.0Sleep With Mozart0.0454NaN0.1080.1110.97300.1020NaN0.797000-20.757Horn Concerto No. 1 in D, H.VIId No. 3: 2. Adagio
1799376IaSUc4wx1rzo8lGs4KOotNaN56411.0['corecore', 'slowed and reverb', 'weirdcore']1.0NaN56.0False142.568NaN2021-01-010.255010.02022-10-19T17:42:33Z64.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0School Rooftop (Slowed Down Version)0.0534NaN0.7400.2310.27500.1050NaN0.894000-7.293School Rooftop - Slowed Down Version
1799387uEqtkXR2ZhHywbFwSS40MNaN1204948.0['disco', 'funk', 'motown', 'p funk', 'quiet storm', 'soul', 'synth funk']13.0NaN56.0False122.157NaN19820.87904.0NaN40.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN0.04.0Throwin' Down (Expanded Edition)0.0369NaN0.7560.9060.15900.4290NaN0.001110-6.348Dance Wit' Me
1799395IPGZpdsDNz4Td1cb9HfzxNaN7047.0['lo-fi vgm']36.0NaN50.0False104.622NaN2024-01-190.08255.02024-01-19T12:31:00Z27.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0Mario & Sleep0.0460NaN0.6690.3220.99000.1100NaN0.963000-22.644Galaxy 2 Prologue
1799406FeCT5THgV7fdWUSHUvEjNNaN605.0[]1.0NaN3.0False88.489NaN2016-12-120.72000.0NaN0.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN0.04.0Summer Friend0.0408NaN0.6270.5390.29200.2330NaN0.017900-6.193Summer Friend